Journal articles: 'Autonomous vehicle systems'

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Yaakub, Salma, and Mohammed Hayyan Alsibai. "A Review on Autonomous Driving Systems." International Journal of Engineering Technology and Sciences 5, no. 1 (June 20, 2018): 1–16. http://dx.doi.org/10.15282/ijets.v5i1.2800.

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Autonomous vehicles are one of the promising solutions to reduce traffic crashes and improve mobility and traffic system. An autonomous vehicle is preferable because it helps in reducing the need for redesigning the infrastructure and because it improves the vehicle power efficiency in terms of cost and time taken to reach the destination. Autonomous vehicles can be divided into 3 types: Aerial vehicles, ground vehicles and underwater vehicles. General, four basic subsystems are integrated to enable a vehicle to move by itself which are: Position identifying and navigation system, surrounding environment situation analysis system, motion planning system and trajectory control system. In this paper, a review on autonomous vehicles and their related technological applications is presented to highlight the aspects of this industry as a part of industry 4.0 concept. Moreover, the paper discusses the best autonomous driving systems to be applied on our wheelchair project which aims at converting a manual wheelchair to a smart electric wheelchair

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Creating Autonomous Vehicle Systems." Synthesis Lectures on Computer Science 6, no. 1 (October 25, 2017): i—186. http://dx.doi.org/10.2200/s00787ed1v01y201707csl009.

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R. Sushma and J. Satheesh Kumar. "Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems." December 2022 4, no. 4 (January 9, 2023): 307–15. http://dx.doi.org/10.36548/jeea.2022.4.007.

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The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.

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Thieme, Christoph Alexander, and Ingrid Bouwer Utne. "A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 4 (August 2017): 446–64. http://dx.doi.org/10.1177/1748006x17709377.

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Autonomous marine systems, such as autonomous ships and autonomous underwater vehicles, gain increased interest in industry and academia. Expected benefits of autonomous marine system in comparison to conventional marine systems are reduced cost, reduced risk to operators, and increased efficiency of such systems. Autonomous underwater vehicles are applied in scientific, commercial, and military applications for surveys and inspections of the sea floor, the water column, marine structures, and objects of interest. Autonomous underwater vehicles are costly vehicles and may carry expensive payloads. Hence, risk models are needed to assess the mission success before a mission and adapt the mission plan if necessary. The operators prepare and interact with autonomous underwater vehicles to carry out a mission successfully. Risk models need to reflect these interactions. This article presents a Bayesian belief network to assess the human–autonomy collaboration performance, as part of a risk model for autonomous underwater vehicle operation. Human–autonomy collaboration represents the joint performance of the human operators in conjunction with an autonomous system to achieve a mission aim. A case study shows that the human–autonomy collaboration can be improved in two ways: (1) through better training and inclusion of experienced operators and (2) through improved reliability of autonomous functions and situation awareness of vehicles. It is believed that the human–autonomy collaboration Bayesian belief network can improve autonomous underwater vehicle design and autonomous underwater vehicle operations by clarifying relationships between technical, human, and organizational factors and their influence on mission risk. The article focuses on autonomous underwater vehicle, but the results should be applicable to other types of autonomous marine systems.

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Wallaschek, Jörg, Hendrik Honsel, and Michael Kleinkes. "Autonomous vehicle front lighting systems." International Journal of Vehicle Autonomous Systems 10, no. 3 (2012): 256. http://dx.doi.org/10.1504/ijvas.2012.051248.

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Islam, Mhafuzul, Mashrur Chowdhury, Hongda Li, and Hongxin Hu. "Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 31, 2019): 494–507. http://dx.doi.org/10.1177/0361198119855606.

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Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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Oliveira, Luis, Karl Proctor, Christopher G. Burns, and Stewart Birrell. "Driving Style: How Should an Automated Vehicle Behave?" Information 10, no. 6 (June 25, 2019): 219. http://dx.doi.org/10.3390/info10060219.

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This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.

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Oktay, Tugrul, Harun Celik, and Ilke Turkmen. "Maximizing autonomous performance of fixed-wing unmanned aerial vehicle to reduce motion blur in taken images." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 7 (March 28, 2018): 857–68. http://dx.doi.org/10.1177/0959651818765027.

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In this study, reducing motion blur in images taken by our unmanned aerial vehicle is investigated. Since shakes of unmanned aerial vehicle cause motion blur in taken images, autonomous performance of our unmanned aerial vehicle is maximized to prevent it from shakes. In order to maximize autonomous performance of unmanned aerial vehicle (i.e. to reduce motion blur), initially, camera mounted unmanned aerial vehicle dynamics are obtained. Then, optimum location of unmanned aerial vehicle camera is estimated by considering unmanned aerial vehicle dynamics and autopilot parameters. After improving unmanned aerial vehicle by optimum camera location, dynamics and controller parameters, it is called as improved autonomous controlled unmanned aerial vehicle. Also, unmanned aerial vehicle with camera fixed at the closest point to center of gravity is called as standard autonomous controlled unmanned aerial vehicle. Both improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles are performed in real time flights, and approximately same trajectories are tracked. In order to compare performance of improved autonomous controlled and standard autonomous controlled unmanned aerial vehicles in reducing motion blur, a motion blur kernel model which is derived using recorded roll, pitch and yaw angles of unmanned aerial vehicle is improved. Finally, taken images are simulated to examine effect of unmanned aerial vehicle shakes. In comparison with standard autonomous controlled flight, important improvements on reducing motion blur are demonstrated by improved autonomous controlled unmanned aerial vehicle.

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Ding, Zhizhong, Chao Sun, Momiao Zhou, Zhengqiong Liu, and Congzhong Wu. "Intersection Vehicle Turning Control for Fully Autonomous Driving Scenarios." Sensors 21, no. 12 (June 9, 2021): 3995. http://dx.doi.org/10.3390/s21123995.

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Currently the research and development of autonomous driving vehicles (ADVs) mainly consider the situation whereby manual driving vehicles and ADVs run simultaneously on lanes. In order to acquire the information of the vehicle itself and the environment necessary for decision-making and controlling, the ADVs that are under development now are normally equipped with a lot of sensing units, for example, high precision global positioning systems, various types of radar, and video processing systems. Obviously, the current advanced driver assistance systems (ADAS) or ADVs still have some problems concerning high reliability of driving safety, as well as the vehicle’s cost and price. It is certain, however, that in the future there will be some roads, areas or cities where all the vehicles are ADVs, i.e., without any human driving vehicles in traffic. For such scenarios, the methods of environment sensing, traffic instruction indicating, and vehicle controlling should be different from that of the situation mentioned above if the reliability of driving safety and the production cost expectation is to be improved significantly. With the anticipation that a more sophisticated vehicle ad hoc network (VANET) should be an essential transportation infrastructure for future ADV scenarios, the problem of vehicle turning control based on vehicle to everything (V2X) communication at road intersections is studied. The turning control at intersections mainly deals with three basic issues, i.e., target lane selection, trajectory planning and calculation, and vehicle controlling and tracking. In this paper, control strategy, model and algorithms are proposed for the three basic problems. A model predictive control (MPC) paradigm is used as the vehicle upper layer controller. Simulation is conducted on the CarSim-Simulink platform with typical intersection scenes.

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Wu, Shu Yun, and Xu Hao Lv. "Four-Rotor Autonomous Vehicle." Applied Mechanics and Materials 505-506 (January 2014): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.286.

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Four rotary-wing micro air vehicles use four motors as the power unit, by adjusting the motor speed control flight of underactuated systems [. In order to achieve four-rotor autonomous vehicle autonomous flight control, preliminary design of flight control system, and use F5F100LEA single-chip as computer control unit, Proposed the flight system hardware design. Vehicle has the advantages of light weight, small size, low power consumption. After several laboratory tests, the design and reliable performance, to meet the aircraft take off, hover, landing flight mode control requirements.

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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, and Jaroslav Hlava. "Model Predictive Control for Autonomous Driving Vehicles." Electronics 10, no. 21 (October 24, 2021): 2593. http://dx.doi.org/10.3390/electronics10212593.

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The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

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Özdemir, Özge, İslam Kılıç, Ahmet Yazıcı, and Kemal Özkan. "A V2V System Module for Inter Vehicle Communication." Applied Mechanics and Materials 850 (August 2016): 16–22. http://dx.doi.org/10.4028/www.scientific.net/amm.850.16.

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An advanced driver assistance system (ADAS) is the premium technology for autonomous driving. It uses data from vision/camera systems, data from in vehicle sensors, and data from vehicle-to-vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication systems. The next generation systems even autonomous vehicles are expected to use the V2V information to increase the safety for non-line of sight environments. Exchanging some data like vehicle position, speed, status etc., helps to the driver about potential problems, or to avoid collisions. In this paper, a V2V communication system module is designed and tested on the vehicles.

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Alsuwian, Turki, Mian Hamza Usman, and Arslan Ahmed Amin. "An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network." Electronics 11, no. 19 (October 1, 2022): 3165. http://dx.doi.org/10.3390/electronics11193165.

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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.

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Pokorny, Jiri, Khanh Ma, Salwa Saafi, Jakub Frolka, Jose Villa, Mikhail Gerasimenko, Yevgeni Koucheryavy, and Jiri Hosek. "Prototype Design and Experimental Evaluation of Autonomous Collaborative Communication System for Emerging Maritime Use Cases." Sensors 21, no. 11 (June 3, 2021): 3871. http://dx.doi.org/10.3390/s21113871.

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Automated systems have been seamlessly integrated into several industries as part of their industrial automation processes. Employing automated systems, such as autonomous vehicles, allows industries to increase productivity, benefit from a wide range of technologies and capabilities, and improve workplace safety. So far, most of the existing systems consider utilizing one type of autonomous vehicle. In this work, we propose a collaboration of different types of unmanned vehicles in maritime offshore scenarios. Providing high capacity, extended coverage, and better quality of services, autonomous collaborative systems can enable emerging maritime use cases, such as remote monitoring and navigation assistance. Motivated by these potential benefits, we propose the deployment of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV) in an autonomous collaborative communication system. Specifically, we design high-speed, directional communication links between a terrestrial control station and the two unmanned vehicles. Using measurement and simulation results, we evaluate the performance of the designed links in different communication scenarios and we show the benefits of employing multiple autonomous vehicles in the proposed communication system.

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Mitrović, Miloš, Vladimir Popović, and Dragan Stamenković. "Implementation of traffic sign recognition on the scaled vehicle model." Industrija 50, no. 2 (2022): 51–60. http://dx.doi.org/10.5937/industrija50-41958.

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The popularity of autonomous vehicles has grown in the past few years as autonomous systems are more and more present on vehicles. The most accessible way for students of mechanical and software engineers to learn about autonomous vehicles is by applying algorithms and systems necessary for autonomous driving on the scaled vehicle model. These models are, as in this case, and are equipped with all systems necessary for autonomous driving, such as a four-wheel drive powertrain, a suspension system, an electrically controlled steering system, a brain-computer and a camera. The goal of projects such as this one is to make the vehicle capable of autonomous driving on a designated track, obeying regular traffic rules and signs (for example, the vehicle has to perform a full stop when it approaches the stop sign). To make this possible, it is necessary for a vehicle to "know" which traffic sign is nearby, i.e., traffic sign recognition is required. For this purpose, traffic sign recognition is done by an artificial neural network. The training process of the proper artificial neural network will be shown in this paper.

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Kołodziejska, Agata, Karolina Krzykowska, and Mirosław Siergiejczyk. "Comparative Analysis of V2V and A2A Technologies." Journal of KONBiN 45, no. 1 (March 1, 2018): 345–64. http://dx.doi.org/10.2478/jok-2018-0018.

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Abstract In recent years, around the world, there has been work underway on systems, which will increase not only the comfort of traveling but, above all, the safety and reliability of the road traffic. The systems in this field, designed to replace human beings in the future, thus eliminating their mistakes on the road, already have their prototypes. However, these prototypes are still being improved and require a lot of work so they could operate fully and reliably. The subject of the publication is a compilation of two new concepts in the field of Intelligent Transport Systems. These concepts are V2V (Vehicle - to - Vehicle) and A2A (Autonomous vehicle - to - Autonomous vehicle). Their comparison was carried out in terms of functionality, communication, vehicle equipment, legal aspects and the anticipated date of their entry into the market. Also examples of first tests and implementations of vehicles with driver assistance systems, and semi-autonomous vehicles were presented.

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Stania, Marek. "Mechatronics Systems of Autonomous Transport Vehicle." Solid State Phenomena 198 (March 2013): 96–101. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.96.

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The purpose of this paper is to present the development and realization of the elaborate mechatronic systems, having its main application in the logistic industry. The innovative, patented steering system is its unique feature. The steerage is based on the torque difference between the drive wheels. This solution allows for the unlimited maneuverability during the motion of the vehicle.

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Liu, Shaoshan, Liyun Li, Jie Tang, Shuang Wu, and Jean-Luc Gaudiot. "Creating Autonomous Vehicle Systems, Second Edition." Synthesis Lectures on Computer Science 8, no. 2 (September 9, 2020): i—216. http://dx.doi.org/10.2200/s01036ed1v01y202007csl012.

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D’Angelo, Vincenzo, Paolo Folino, Marco Lupia, Gianfranco Gagliardi, Gianni Cario, Francesco Cicchello Gaccio, and Alessandro Casavola. "A ROS-Based GNC Architecture for Autonomous Surface Vehicle Based on a New Multimission Management Paradigm." Drones 6, no. 12 (November 27, 2022): 382. http://dx.doi.org/10.3390/drones6120382.

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This paper presents the design and implementation of BAICal (Intelligent Autonomous Buoy by the University of Calabria), an autonomous surface vehicle (ASV) developed at the Autonomous Systems Lab (LASA) of the Department of Computer Science, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria. The basic project was born as a research program in marine robotics with multiple applications, either in the sea or in lake/river environments, for data monitoring, search and rescue operations and diver support tasks. Mechanical and hardware designs are discussed by considering a three-degree-of-freedom (3DoF) dynamical model of the vehicle. An extension to the typical guidance, navigation, and control (GNC) software architecture is presented. The software design and the implementation of a manager module (M-GNC architecture) that allows the vehicle to autonomously coordinate missions are described. Indeed, autonomous guidance and movement are only one of several more complex tasks that mobile robots have to perform in a real scenario and that allow a long-term life cycle. Module-based software architecture is developed by using the Robot Operating System (ROS) framework that is suitable for different kinds of autonomous vehicles, such as aerial, ground, surface or underwater drones.

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Cao, Hang, and Máté Zöldy. "An Investigation of Autonomous Vehicle Roundabout Situation." Periodica Polytechnica Transportation Engineering 48, no. 3 (August 4, 2019): 236–41. http://dx.doi.org/10.3311/pptr.13762.

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The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.

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Schulte, Joseph, Mark Kocherovsky, Nicholas Paul, Mitchell Pleune, and Chan-Jin Chung. "Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition." Vehicles 4, no. 1 (March 9, 2022): 243–58. http://dx.doi.org/10.3390/vehicles4010016.

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Leader-follower autonomy (LFA) systems have so far only focused on vehicles following other vehicles. Though there have been several decades of research into this topic, there has not yet been any work on human-vehicle leader-follower systems in the known literature. We present a system in which an autonomous vehicle—our ACTor 1 platform—can follow a human leader who controls the vehicle through hand-and-body gestures. We successfully developed a modular pipeline that uses artificial intelligence/deep learning to recognize hand-and-body gestures from a user in view of the vehicle’s camera and translate those gestures into physical action by the vehicle. We demonstrate our work using our ACTor 1 platform, a modified Polaris Gem 2. Results show that our modular pipeline design reliably recognizes human body language and translates the body language into LFA commands in real time. This work has numerous applications such as material transport in industrial contexts.

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Nakrani, Naitik, and Maulin M. Joshi. "An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 687. http://dx.doi.org/10.11591/ijai.v10.i3.pp687-697.

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In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.

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Healey, A. J. "Model-Based Maneuvering Controls for Autonomous Underwater Vehicles." Journal of Dynamic Systems, Measurement, and Control 114, no. 4 (December 1, 1992): 614–22. http://dx.doi.org/10.1115/1.2897733.

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This paper proposes the development of a model following autopilot system for an Autonomous Underwater Vehicle (AUV) depth changing control. The parameters to command a maneuver are generated off-line and selected as appropriate by the vehicle’s autonomous control system. A series of such preprogrammed maneuvers can be stored in an on-board computer, and used as command generation systems for the autopilot. The paper presents a linear model following control (LMFC) design based on the open-loop linearized vehicle model as the reference model, a robustness analysis of the scheme and simulation results of response in the diveplane using the full nonlinear vehicle system equations. LMFC has been proposed for aircraft where certain desirable handling characteristics based on an arbitrary model are required or where decoupled control for Control Configured Vehicle (CCV) performance is needed. It is shown here that this model-based LMFC autopilot for underwater vehicles exhibits relatively robust behavior under conditions of parameter uncertainty and non-linearity which is not worse than the equivalent LQR/LTR for linear output feedback systems. Also, a tailored transient response is provided, conducive to near time optimal response.

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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, and Michal Petru. "Feasible Trajectories Generation for Autonomous Driving Vehicles." Applied Sciences 11, no. 23 (November 24, 2021): 11143. http://dx.doi.org/10.3390/app112311143.

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This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.

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Wöber, Wilfried, Georg Novotny, Lars Mehnen, and Cristina Olaverri-Monreal. "Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics." Applied Sciences 10, no. 18 (September 10, 2020): 6317. http://dx.doi.org/10.3390/app10186317.

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On-board sensory systems in autonomous vehicles make it possible to acquire information about the vehicle itself and about its relevant surroundings. With this information the vehicle actuators are able to follow the corresponding control commands and behave accordingly. Localization is thus a critical feature in autonomous driving to define trajectories to follow and enable maneuvers. Localization approaches using sensor data are mainly based on Bayes filters. Whitebox models that are used to this end use kinematics and vehicle parameters, such as wheel radii, to interfere the vehicle’s movement. As a consequence, faulty vehicle parameters lead to poor localization results. On the other hand, blackbox models use motion data to model vehicle behavior without relying on vehicle parameters. Due to their high non-linearity, blackbox approaches outperform whitebox models but faulty behaviour such as overfitting is hardly identifiable without intensive experiments. In this paper, we extend blackbox models using kinematics, by inferring vehicle parameters and then transforming blackbox models into whitebox models. The probabilistic perspective of vehicle movement is extended using random variables representing vehicle parameters. We validated our approach, acquiring and analyzing simulated noisy movement data from mobile robots and vehicles. Results show that it is possible to estimate vehicle parameters with few kinematic assumptions.

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Abu El-Sebah, Mohamed I., Fathy A. Syam, Emad A. Sweelem, Mohamed M. El-Sotouhy, and Mohamed M. El Sotouhy. "A Proposed Controller for an Autonomous Vehicles Embedded System." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 22 (February 7, 2023): 1–9. http://dx.doi.org/10.37394/23201.2023.22.1.

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Many research have observable development in the automated vehicle driving field during the last few decades. This research proposed a simple optimum Intelligent PID (SO PID) controller to simplify the automated vehicle motion control. Control of an autonomous vehicle’s steering routines plays an essential key role. Several steering control procedures are proposed that improve automated vehicle performance. The design of secure embedded control systems must overcome the difficulties associated with designing both computing and control systems. Also, this research introduces a model of the autonomous car prototype controlled via an Arduino microcontroller board and the GPS Module to receive the car coordinates. The car moves safely, and autonomously consequently avoiding the risk of human faults. Several algorithms such as angle and distance calculations to the waypoint and obstacle detection are combined to control the car movement.

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Hao, Mingyang, Yanyan Li, and Toshiyuki Yamamoto. "Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan." Smart Cities 2, no. 2 (June 11, 2019): 230–44. http://dx.doi.org/10.3390/smartcities2020015.

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Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

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Galvao, Luiz G., Maysam Abbod, Tatiana Kalganova, Vasile Palade, and Md Nazmul Huda. "Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review." Sensors 21, no. 21 (October 31, 2021): 7267. http://dx.doi.org/10.3390/s21217267.

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Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

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Lee, Heung-Gu, Dong-Hyun Kang, and Deok-Hwan Kim. "Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles." Electronics 10, no. 19 (October 1, 2021): 2405. http://dx.doi.org/10.3390/electronics10192405.

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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver’s situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver’s situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

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Babicheva, Tatiana, Wilco Burghout, Ingmar Andreasson, and Nadege Faul. "The matching problem of empty vehicle redistribution in autonomous taxi systems." International Journal of Traffic and Transportation Management 1, no. 1 (June 10, 2019): 1–8. http://dx.doi.org/10.5383/jttm.01.01.001.

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Vedachalam, Narayanaswamy, Raju Ramesh, Vandavasi Bala Naga Jyothi, Vittal Doss Prakash, Gidugu Ananda Ramadass, and Malayath Aravindakshan Atmanand. "Design Considerations for Strategic Autonomous Underwater Swarm Robotic Systems." Marine Technology Society Journal 54, no. 2 (March 1, 2020): 25–34. http://dx.doi.org/10.4031/mtsj.54.2.6.

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AbstractAutonomous underwater swarm robotic systems (AU-SRS) are vital for exploration of the vast marine resources, spatio-temporal monitoring of the oceans for understanding the changing climate patterns, marine pollution monitoring, defense, and identification of assets lost in the oceans. The paper summarizes the technological developments in the autonomous underwater vehicles hitherto and discusses the design requirements for next-generation intelligent AU-SRS including intra-vehicle intelligence, inter-vehicle communication, intervention capability, swarm algorithms, and bio-inspired designs. The importance of quality-centered system engineering is also detailed.

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Tranter, Kieran Mark. "The Challenges of Autonomous Motor Vehicles for Queensland Road and Criminal Laws." QUT Law Review 16, no. 2 (June 17, 2016): 59. http://dx.doi.org/10.5204/qutlr.v16i2.626.

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This article examines the challenges of autonomous motor vehicles for Queensland road and criminal laws. Autonomous vehicles refer to motor vehicles where driver decision making has been augmented or replaced by intelligent systems. Proponents of autonomous vehicles argue that they will virtually eliminate road accidents, boost productivity and provide significant environmental benefits. The key issue is that autonomous vehicles challenge the notion of human responsibility which lies at the core of Queensland’s road and criminal laws. The road rules are predicated on a driver in control of the vehicle, the intoxication regime is concerned with the person in charge of the vehicle and the dangerous driving offences require a person who operates a vehicle. Notwithstanding this challenge, it can be seen that much of Queensland’s law is adaptable to autonomous vehicles. However, there are some identifiable anomalies that require reform.

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S, Thylashri, Manikandaprabu N, Jayakumar T, Vijayachitra S, and Kiruthiga G. "Effective Techniques for Pedestrian Detection in Smart Autonomous Vehicles." Webology 18, no. 05 (October 29, 2021): 1176–83. http://dx.doi.org/10.14704/web/v18si05/web18298.

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Pedestrians are essential objects in computer vision. Pedestrian detection in images or videos plays an important role in many applications such as real-time monitoring, counting pedestrians at various events, detecting falls of the elderly, etc. It is formulated as a problem of the automatic identification and location of pedestrians in pictures or videos. In real images, the art of pedestrian detection is an important task for major applications such as video surveillance, autonomous driving systems, etc. Pedestrian detection is also an important feature of the autonomous vehicle driving system because it identifies pedestrians and minimizes accidents between vehicles and pedestrians. The research trend in the field of vehicle electronics and driving safety, vision-based pedestrian recognition technologies for smart vehicles have established themselves loudly or slowing down the vehicle. In general, the visual pedestrian detection progression capable of be busted down into three consecutive steps: pedestrian detection, pedestrian recognition, and pedestrian tracking. There is also visual pedestrian recognition in the vehicle. Finally, we study the challenges and evolution of research in the future.

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Abramov, Stanislav, Oleh Lupalenko, and Gennady Belous. "AUTONOMOUS MOVEMENT AND CONTROL SYSTEM OF A MEDIUM-DUTY MILITARY VEHICLE." Collection of scientific works of Odesa Military Academy, no. 16 (February 11, 2022): 191–200. http://dx.doi.org/10.37129/2313-7509.2021.16.191-200.

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The article considers the prospects of creating new types of military vehicles, including autonomous vehicles for military needs, analyzes foreign and domestic experience in the field of unmanned vehicles. The classification of levels of autonomy among the systems of assistance to the driver of modern cars is given. The technology of movement of unmanned vehicles in the «Leader-Follower» mode and the set of equipment that ensures its operation are revealed. Emphasis is placed on creating a system of autonomous movement and control of a medium-ton military vehicle and its features. The construction of the architecture of the system of autonomous movement and control of a medium-ton military vehicle is given, as well as the tasks and requirements for the subsystems that are part of it are determined. The general principle of operation of the autonomous movement and control system and the content of information processing operations that it performs are revealed. Based on the analysis, the technical characteristics of LiDAR sensors as components of the elements of the technical vision system of the autonomous motion subsystem are given. The general principle of operation of LiDAR technology and possibilities which are reached at its application in systems of autonomous movement are opened. The need to integrate various sensors to detect and recognize objects, obstacles and control the autonomous movement of the car is indicated. Keywords: medium-duty military vehicle, unmanned vehicle, autonomous movement, autonomous movement and control system, subsystem, autonomous movement subsystem, integrated navigation subsystem, control subsystem, elements of technical vision system, image interpretation unit, sensor, scanner, LiDAR, object recognition, information processing, digital elevation model, sensor integration.

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Tang, Sarah, and Vijay Kumar. "Autonomous Flight." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 29–52. http://dx.doi.org/10.1146/annurev-control-060117-105149.

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This review surveys the current state of the art in the development of unmanned aerial vehicles, focusing on algorithms for quadrotors. Tremendous progress has been made across both industry and academia, and full vehicle autonomy is now well within reach. We begin by presenting recent successes in control, estimation, and trajectory planning that have enabled agile, high-speed flight using low-cost onboard sensors. We then examine new research trends in learning and multirobot systems and conclude with a discussion of open challenges and directions for future research.

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Hegedűs, Támas, Balázs Németh, and Péter Gáspár. "Graph-based Multi-Vehicle Overtaking Strategy for Autonomous Vehicles." IFAC-PapersOnLine 52, no. 5 (2019): 372–77. http://dx.doi.org/10.1016/j.ifacol.2019.09.060.

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Laconte, Johann, Abderrahim Kasmi, Romuald Aufrère, Maxime Vaidis, and Roland Chapuis. "A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios." Sensors 22, no. 1 (December 30, 2021): 247. http://dx.doi.org/10.3390/s22010247.

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In the context of autonomous vehicles on highways, one of the first and most important tasks is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take into account the information from several sensors and fuse them with data coming from road maps. The localization problem on highways can be distilled into three main components. The first one consists of inferring on which road the vehicle is currently traveling. Indeed, Global Navigation Satellite Systems are not precise enough to deduce this information by themselves, and thus a filtering step is needed. The second component consists of estimating the vehicle’s position in its lane. Finally, the third and last one aims at assessing on which lane the vehicle is currently driving. These two last components are mandatory for safe driving as actions such as overtaking a vehicle require precise information about the current localization of the vehicle. In this survey, we introduce a taxonomy of the localization methods for autonomous vehicles in highway scenarios. We present each main component of the localization process, and discuss the advantages and drawbacks of the associated state-of-the-art methods.

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Andersen, Tobias, Ananda Nielsen, Matteo Fumagalli, Joachim Axelsen, Mads Christiansen, Ole Ravn, and Nils Andersen. "Modular Design and Implementation for Rapid Deployment of Autonomous Systems." Field Robotics 2, no. 1 (March 10, 2022): 1951–70. http://dx.doi.org/10.55417/fr.2022063.

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This paper describes the autonomous systems, which Technical University of Denmark used to participate in Challenge 2 of the Mohamed Bin Zayed International Robotics Challenge. We participated with two autonomous vehicles in the Challenge: an aerial and a ground vehicle. The mission of both of the vehicles was to locate blocks and use them to build a wall in a marked location. Our ground solution consisted of a SKID steered vehicle, with a Universal Robots arm attached to it, and our aerial solution was a DJI M100 quadrotor in X configuration, equipped with a rangefinder and camera. Both platforms each have their own custom build end-effector, designed for lifting flat magnetic objects. The software was designed with a modular approach based on the mobotware framework, such that mission scripts could rapidly be assembled at the deployment site. A state of the art neural network, for detecting blocks, was trained for our ground vehicle. The effectiveness of the modular approach was tested in the challenge, and our lessons learned is included in the paper.

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Erginli, Mustafa, and Ibrahim Cil. "Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles." Systems 10, no. 3 (June 15, 2022): 83. http://dx.doi.org/10.3390/systems10030083.

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Real-time route tracking is an important research topic for autonomous vehicles used in industrial facilities. Traditional methods such as copper line tracking on the ground, wireless guidance systems, and laser systems are still used in route tracking. In this study, a deep-learning-based floor path model for route tracking of autonomous vehicles is proposed. A deep-learning floor path model and algorithm have been developed for highly accurate route tracking, which avoids collisions of vehicles and follows the shortest route to reach the destination. The floor path model consists of markers. Routes in the floor path model are created by using these markers. The floor path model is transmitted to autonomous vehicles as a vector by a central server. The server dispatches the target marker address to the vehicle to move. The vehicle calculates all possible routes to this address and chooses the shortest one. Marker images on the selected route are processed using image processing and classified with a pre-trained deep-CNN model. If the classified image and the image on the selected route are the same, the vehicle proceeds toward its destination. While the vehicle moves on the route, it sends the last classified marker to the server. Other autonomous vehicles use this marker to determine the location of this vehicle. Other vehicles on the route wait to avoid a collision. As a result of the experimental studies we have carried out, the route tracking of the vehicles has been successfully achieved.

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Alpos, Theodoros, Christina Iliopoulou, and Konstantinos Kepaptsoglou. "Nature-Inspired Optimal Route Network Design for Shared Autonomous Vehicles." Vehicles 5, no. 1 (December 24, 2022): 24–40. http://dx.doi.org/10.3390/vehicles5010002.

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Emerging forms of shared mobility call for new vehicle routing models that take into account vehicle sharing, ride sharing and autonomous vehicle fleets. This study deals with the design of an optimal route network for autonomous vehicles, considering both vehicle sharing and ride sharing. The problem is modeled as a one-to-many-to-one vehicle routing problem with vehicle capacity and range constraints. An ant colony optimization algorithm is applied to the problem in order to construct a set of routes that satisfies user requests under operational constraints. Results show that the algorithm is able to produce solutions in relatively short computational times, while exploiting the possibility of ride sharing to reduce operating costs. Results also underline the potential of exploiting shared autonomous vehicles in the context of a taxi service for booking trips through electronic reservation systems.

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Dam, Koen H. Van, Zofia Verwater-Lukszo, Jaap A. Ottjes, and Gabriel Lodewijks. "Distributed intelligence in autonomous multi-vehicle systems." International Journal of Critical Infrastructures 2, no. 2/3 (2006): 261. http://dx.doi.org/10.1504/ijcis.2006.009442.

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Alonso, Javier, Vicente Milanés, Joshué Pérez, Enrique Onieva, Carlos González, and Teresa de Pedro. "Autonomous vehicle control systems for safe crossroads." Transportation Research Part C: Emerging Technologies 19, no. 6 (December 2011): 1095–110. http://dx.doi.org/10.1016/j.trc.2011.06.002.

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Kang, David S., Jamie M. Anderson, and Paul A. DeBitetto. "Draper unmanned vehicle systems." Robotica 18, no. 3 (May 2000): 263–72. http://dx.doi.org/10.1017/s0263574799002246.

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Draper Small Autonomous Aerial Vehicle (DSAAV), MITy and SMART micro-rovers, Companion mini-rover, and Vorticity Control Unmanned Undersea Vehicle (VCUUV) are highlighted. DSAAV demonstrated autonomy with GPS/INS integration and vision processing. The micro- and mini-rovers investigated ground based autonomy with extensive mapping and planning integration. VCUUV is a flexible-hull UUV which propels and maneuvers like a tuna.

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Bin Issa, Razin, Modhumonty Das, Md Saferi Rahman, Monika Barua, Md Khalilur Rhaman, Kazi Shah Nawaz Ripon, and Md Golam Rabiul Alam. "Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment." Sensors 21, no. 4 (February 20, 2021): 1468. http://dx.doi.org/10.3390/s21041468.

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Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

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Orgován, László, Tamás Bécsi, and Szilárd Aradi. "Autonomous Drifting Using Reinforcement Learning." Periodica Polytechnica Transportation Engineering 49, no. 3 (September 1, 2021): 292–300. http://dx.doi.org/10.3311/pptr.18581.

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Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

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Turoń, Katarzyna, and Andrzej Kubik. "Economic Aspects of Driving Various Types of Vehicles in Intelligent Urban Transport Systems, Including Car-Sharing Services and Autonomous Vehicles." Applied Sciences 10, no. 16 (August 12, 2020): 5580. http://dx.doi.org/10.3390/app10165580.

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Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept in the automotive market, we dedicated this article to the idea of using autonomous vehicles as a part of car-sharing systems in intelligent, urban transport systems. The research herein is focused on the economic aspects of using autonomous vehicles in comparison to the classic car fleet available in car-sharing systems and to vehicles that belong to individual owners. We present our method for appropriate fleet selection based on the Delphi method and the calculations made through a scientific experiment performed based on Hartley’s plan. The results indicate the relation of travel parameters (including vehicle type) to the total cost of travel in urban transport systems. We also present the main terms related to autonomous vehicles. This article provides support for people who want to deepen knowledge about autonomous vehicles and new mobility solutions used in urban transport systems.

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Balkmar, Dag, and Ulf Mellström. "Masculinity and Autonomous Vehicles." Transfers 8, no. 1 (March 1, 2018): 44–63. http://dx.doi.org/10.3167/trans.2018.080105.

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This article addresses the anthropomorphization and interpellative experience of cars and trucks, in order to meet future mobility challenges. Autonomous vehicles offer an emancipatory opportunity within a wider movement of degendering and regendering motor vehicles. We argue that autonomous vehicles can challenge the foundations of a gendered economy founded on masculinity, speed, pleasure, and embodiment. Rather than thinking in terms of a process of demasculinization, this article anticipates a regendering and resegregation through which certain forms of masculine gendered economies of pleasure will lose ground and others will gain. A core question in this article asks who will be in the driver’s seat of future systems of automobility as the control of the vehicle is gradually being transferred from the driver to digital control systems and intelligent roads.

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Funkhouser, Kelly, and Frank Drews. "Putting the Brakes on Autonomous Vehicle Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 1859–63. http://dx.doi.org/10.1177/1541931213601424.

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The assimilation of automation in commuter vehicles is rapidly increasing, as too are the concerns with these technologies. Human interaction with autonomous vehicles must be thoroughly researched to understand the quantification and qualification of interactive behaviors with these systems. We developed a study using a high-fidelity driving simulator to mimic probable breakdowns with these systems to better understand the subsequent human responses and to explore the necessary technological requirements to overcome potential problems. 30 participants engaged in a driving scenario switching between manual and autonomous vehicle control. We accounted for individual differences in braking reaction time while simultaneously engaging in a secondary cognitive task during times of autonomous vehicle control. Results show the average RT for baseline scenarios without the cognitive task was 832.1 milliseconds while the average RT for baseline scenarios with the cognitive task was 908.4 milliseconds; a 9.17% significant increase. The average RT for the autonomous scenario was 1357.0 milliseconds; a significant increase of 49.38% over the baseline scenario with the cognitive task that can be attributed to the addition of automation. We found a positive linear correlation of time spent in autonomous control and subsequent braking reaction time. Additionally, cognitive task difficulty, attention allocation, self-reported mental demand, fatigue, and heart rate affect reaction time when cued to take control of the vehicle.

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Guo, Jinghua, Yugong Luo, and Keqiang Li. "Adaptive coordinated collision avoidance control of autonomous ground vehicles." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 9 (May 22, 2018): 1120–33. http://dx.doi.org/10.1177/0959651818774991.

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This article presents a novel coordinated nonlinear adaptive backstepping collision avoidance control strategy for autonomous ground vehicles with uncertain and unmodeled terms. A nonlinear vehicle collision avoidance vehicle model which describes the coupled lateral and longitudinal dynamic features of autonomous ground vehicles is constructed. Then, a modified artificial potential field approach which can ensure that the total potential field of the target is goal minimum, is proposed to produce a collision-free trajectory for autonomous ground vehicles in real-time. Furthermore, in order to handle with the features of coupled and parameter uncertainties of autonomous ground vehicles, an adaptive neural network–based backstepping trajectory tracking control approach is proposed for collision avoidance control system of autonomous ground vehicles, and the stability of this proposed control system is proven by the Lyapunov theory. Finally, the co-simulation and experimental tests are implemented and the results show that the proposed collision avoidance control strategy has excellent tracking performance.

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Yu, Dongyeon, Chanho Park, Hoseung Choi, Donggyu Kim, and Sung-Ho Hwang. "Takeover Safety Analysis with Driver Monitoring Systems and Driver–Vehicle Interfaces in Highly Automated Vehicles." Applied Sciences 11, no. 15 (July 21, 2021): 6685. http://dx.doi.org/10.3390/app11156685.

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According to SAE J3016, autonomous driving can be divided into six levels, and partially automated driving is possible from level three up. A partially or highly automated vehicle can encounter situations involving total system failure. Here, we studied a strategy for safe takeover in such situations. A human-in-the-loop simulator, driver–vehicle interface, and driver monitoring system were developed, and takeover experiments were performed using various driving scenarios and realistic autonomous driving situations. The experiments allowed us to draw the following conclusions. The visual–auditory–haptic complex alarm effectively delivered warnings and had a clear correlation with the user’s subjective preferences. There were scenario types in which the system had to immediately enter minimum risk maneuvers or emergency maneuvers without requesting takeover. Lastly, the risk of accidents can be reduced by the driver monitoring system that prevents the driver from being completely immersed in non-driving-related tasks. We proposed a safe takeover strategy from these results, which provides meaningful guidance for the development of autonomous vehicles. Considering the subjective questionnaire evaluations of users, it is expected to improve the acceptance of autonomous vehicles and increase the adoption of autonomous vehicles.

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Journal articles on the topic 'Autonomous vehicle systems'

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